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Description: Published monthly, online, open-access and having double-blind peer reviewed, American journal of Engineering Research (AJER) is an emerging academic journal in the field of Engineering and Technol...

Published monthly, online, open-access and having double-blind peer reviewed, American journal of Engineering Research (AJER) is an emerging academic journal in the field of Engineering and Technology which deals with all facets of the field of Technology and Engineering. This journal motive and aim is to create awareness, re-shaping the knowledge already created and challenge the existing theories related to the field of Academic Research in any discipline in Technology and Engineering. American journal of Engineering Research (AJER) has a primary aim to publish novel research being conducted and carried out in the domain of Engineering and Technology as a whole. It invites engineering, professors, researchers, professionals, academicians and research scholars to submit their novel and conjectural ideas in the domain of Engineering and Technology in the shape of research articles, book reviews, case studies, review articles and personal opinions that can benefit the engineering and technology researchers in general and society as a whole.

ABSTRACT : The casting industries suffer from low quality production and productivity due to different
aspects of process practices, where various process parameters are involved. A study performed in foundry and
pattern shop of a steel industry explains various challenges that cause defects in casting. This work controls the
sand quality which improves the casting and reduces the defects. Six sigma is control philosophy for reducing
the defect vice versa improves the quality. This work also attempt the improvement in sand quality for Ingot
mould in Foundry and pattern shop of steel industry where such innovative task has not been performed for
achieving the Six Sigma by using the optimization techniques like ANOVA and Taguchi for controlling process
parameters by available soft tool platform MINITAB in research society. Some other study methods also been
performed for refine the results of research like Process Flow Diagram, Why Why Analysis, Cause And Effect
Diagram, Pareto Pie Chart, Analyze Phase, Improve and Control Phase.
Keywords : Six Sigma, DMAIC approach ANOVA, Taguchi .

I.

INTRODUCTION

The term sigma is Greek word used in statistics to represent the standard deviation for a set of data which
denoted by sigma ( σ). The standard deviation provides the variation of measure data. It also gives the process
variation which meets the customer satisfaction. It measure how far a given process deviates from perfection.

Six Sigma – it depends on three factors. Which depend on the context?
First, it’s a level of quality. A statistical basis of measurement: 3.4 defects per million opportunities. So it
works nearly perfectly.
Second, Six Sigma is a problem solving methodology used in any process. Find the root cause and reduce the
defects also it associate costs. It also gives methodology in designing process. It reduces variation in process.
Third, Six Sigma is a management philosophy. It’s based on customers who identify the defects, decrease
customer satisfaction and increase cost. In today’s competitive environment organization provides good service
of highest value with lowest cost. Six -Sigma is a method which gives perfect and practically possible results.

Six- sigma is basically a process based approach in which collects data which includes identify the factors
critical to quality (CTQ). It also disciplined, data driven approach to process improvement and finds variation
associated with each and every process so that it can be improved variation and reduced these variations.
Second concept is defect. In this point define measurable characteristics or output process which is not
acceptable customer limits i.e. goes to beyond in product specification. By using various tools to decrease the
variation and defects to provide service and delivery to meets customer requirements. Sigma level is calculated
in ratio of defects and number of opportunities for defects.

Casting is the first step in the manufacture of metallic component in which the material is liquefied by heating
and poured into previous prepared mould cavity where it is allowed to solidify. Removing the solidified
component from the mould cavity and cleaned to shape. In casting process there are many defects occur, these
defects reduced by different researchers as [1] in a foundry industry. The industry make submersible pumps
components such as Upper housing Motor Pulley, Upper housing, Mini Chaff cutter wheel in large scale and
rejection comes in the form of slag inclusions in cast iron casting. These parameters were chosen for complete
analysis. To minimize the rejection use DMAIC approach. the concept of six sigma [2] which is disciplined,
data-driven methodology that was developed to improve manufacturing quality, company Profitability and
business process. Many organizations have tried to use Six-Sigma DMAIC approach and its tools to get
optimized organizational achievements. The manufacturing industry is explores the level of difficulty and level
of usage of different tools of DMAIC approach. Abidakun et al. [3] paper explains Six Sigma DMAIC analysis
in an aluminum mill in order to identify sources and causes of waste with provide veritable solutions. DMAIC
approaches are justified [4] and minimize sand casting defects when root cause of defect is not traceable.
Business strategy used to improve [5] business and efficiency to meet customer needs and expectations. The
sand castings control the various parameters with DMAIC technique. The results show that the sand casting
rejection due has been reduced from 6.98% to 3.10 % and the defects due to Blow holes were reduced from
2.74% to 0.11% by increasing the permeability and reducing the moisture of sand. Suraj Dhondiram Patil et.al
[6] Use of design of experiments (DOE) and analysis of variance (ANOVA) techniques both are combined to
determine statistically the correlation of defects with the green strength, mould hardness, and pouring rate also
to find their optimum values needed to reduce the defects. Indian foundry rejection rate [7] is one of major
issues, so reduce this rejection by modifying method and design the tool to gives better casting quality and
increase the production cast. A.Kumaravadivel et.al [8] implement the DMAIC based Six Sigma Approach in
order to minimize the occurrence of defects and increase the sigma level of sand casting process.

II.

EXPERIMENTAL SETUP AND PROCEDURE

i.
Define Phase
The ingot mould section produces the ingot moulds required by the plant. Each ingot mould weights 8.8 T or
9.3T and the daily production of ingot moulds are 17 nos. on average. Previous 15 days data are studied and find
some ingot moulds are collapse due to decrease in hardness. For stabilize the hardness find the causes and
eliminate it.

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Fig 2.1Histogram of Plus 1 mm
Figure 2.1shows histogram graph of plus 1 mm. It can be seen from the graph that the mean value of plus 1 mm
sand is 6.793 and standard deviation is 1.107.

Inference- - Figures 2.1 to 2.3 shows the graphics summary which is obtained by using Minitab software. This
Minitab software is used to study the behavior of the process whether the process is normally distributed or not.
In these figures the value of mean and standard deviation is also shown.
After analyze these graph, it is found that the three types of sand composition such as +1 mm, +106mm and 106mm are effective in mould quality and hardness of products. So further investigate there causes of these sand
deviation to improve quality and hardness.
ii.
Measure Phase
a. Cause and Effect DiagramIt was observed that, with the same resources the production on different days are different due to varying cycle
time of the ingot molding so, to find out the root cause of the problem an Ishikawa diagram was constructed as
shown in the figure 2.4.

Fig 2.4 Cause-Effect diagram for Ingot Mould production
The workers and officers engaged with Ingot mould production were discussed for finding out the reasons for
decreasing of hardness. The reasons were discussed based upon 5M’s and environment.
Inference: - The cause and effect diagrams is used to find out the probable causes that are affecting the final
output of the process, in this project after discussing with the group the causes are shown in the figure.
b. Pareto ChartPrevious data of production of ingot mould are gathered and categorized as follows
 Type A
+106 Micron
 Type B
-106 Micron
 Type C
+1 mm
 Type D
Other
A Pareto chart was constructed to know the contributions of various complaints as shown in figure 2.5.

Fig 2.5 Pareto chart of parameter which effect on hardness

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iii.
Analysis Phase
In this phase, why why analysis is used to analyze the problem of machine performance, man power and
material . Analysis is shown in table 2.1 , 2.2 and 2.3 respectively.
WHY WHY analysis
Table2.1 WHY WHY analysis for machine performances
WHY WHY analysis for machine performances
Why?
Answer
Why ingot mould production is Machines are not working well
less?
Why machines are not working Machines are not maintained
properly?
properly
Why machines are not maintained Machines are in use continuously
properly?
Why machines are in use Shortage of machines
continuously?
Table2.2 WHY WHY analysis for man power
WHY WHY analysis for man power
Why?
Answer
Why ingot mould production is In adequate number
less?
manpower
Why less number of man power?
No new recruitment
Why manpower is not utilized Absenteeism
properly?
Why Absenteeism?
Indiscipline
Why indiscipline?
Improper coordination

of

Action
Check machines properly
Machines to be maintained properly
No other option available
Management problem

Action
Beyond Shop control
Effective utilization of manpower
To be improved
Personnel problems
Coordination to be improved

Table 2.3WHY WHY analysis for material
WHY WHY analysis for material
Why?
Answer
Why ingot mould production is No sand in silos
less?
Why silos are not filled?
Machines are not working
properly
Why machines are not working Input sand is not up to the mark
properly?
Why sand quality is not good?
Improper composition
Why improper composition?
Proper sand is not used

Action
Silos to be filled
Machines to be maintained properly
Sand is to be checked
Proper mixing should be there
Allahabad sand is to be purchased

Inference:From why why analysis it is noted that the reason for less production of ingot mould is poor performance of
machines, less number of man power and poor quality of input sand. It is also evident from the above analysis,
the prominent reason for less production of ingot mould is poor quality of input sand whereas other two cases
it is not.
So it was decided to find out the root cause within the sand composition, which is affecting the production
parameters.
Why Why Analysis for other measures such as method, environment etc. are not done as they are not prominent
reasons.
iv.
Improve Phase
This phase statistically reviews the variations in the process and determines what factors significantly contribute
to the output. The main goal of this Improve phase is develop optimal solutions of the problems. The
optimization process involves the large number of key process input variables to determine the greatest impact
few variables of process. Taguchi is a statistics method that aims to understand variation instead of conducting
many experiments and is used to provide experiment runs.

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a. Taguchi-based experimental design
The Taguchi-based experimental design used in this study was an L27 orthogonal array and is shown in Table 4
the controllable factors for orthogonal array design. These control factors were classified low, medium, or high.
The control factors are mainly divided in three levels as show in Table 4.
Process
Parameter

Unit

Level and Value

1 mm sand
+106
micron
sand
-106
micron
sand

mm
micron

1
6.24
22.083

2
7.68
28.066

3
9.12
34.050

micron

2.88

4.04

5.20

b. Analysis of Variance (ANOVA)
Analysis of variance (ANOVA) is used to test significant differences involving two or more means by
comparing variances in groups. ANOVA calculate the amount of variation in a process and determine if it is
significant comes due to random noise which is input that consistently comes randomly and expected variation
in output. Also partitioned the total variation which comes into different sources and compare the variance due
to inconsistency between groups or treatments of variance due to inconsistency between groups.
Source: The source of the variation.
DF: Degrees of freedom associated with each SS (sums of squares, a measure of the variation between the
samples). It measures how much “independent” information is available to calculate each SS.
SS: Amount of variability in the data due to different sources.
MS: The sum of squares divided by their respective degrees of freedom.
F: Determines if the defects of operator, part or operator × part significantly impact the measurement.
v.
Control Phase
The last phase is control phase and the purpose of this phase is to sustain the benefits of the new process and to
ensure that previous problems do not resurface. For complete success of Six Sigma, proper documentation of the
process is recommended. The critical process parameters are continuously monitored and documented to update
the information.
The following actions were planned and taken.
 Maintain proper input sand composition and proper cooling.
 Control dust extraction. Skilled persons required to open and closed the valve.
 Also resin and catalyst composition is mixed with sand is properly.
Sand conveying time, stripping, dry oven, pouring is equal interval of time.

III.

RESULT AND DISCUSSION

On the basis of experimental work, performance measure i.e. hardness is calculated. All the works are
summarize in the form of table. Table 5.1 shows experimental results and S/N ratios for hardness.
Table 3.1 Experimental results and S/N ratios for hardness based on L27 (3^3) array
Run no.

Fig. 3.1 shows the main effect plot for hardness based on the values of S/N ratio. Main effect plot shows the
individual effect of process parameters on the hardness.

Fig 3.1 Main effect plot for hardness based on S/N ratios
Points comes out from main effect plot is listed below;

Effect of 1 mm sand indicated that hardness initially decreases at certain level (i.e. from 6.24 to 7.68) and
after that hardness level increases (from 7.68 to 9.12) as increase the quantity of 1 mm sand. So 1 mm sand has
least significant factor on hardness.

Effect of +106 micron sand behavior is same as 1 mm sand observed that hardness initially decreases at
certain level and after that level of hardness increases as increase the quantity of +106 micron sand and it has
least significant factor on hardness.

-106 micron sand shows the major significant factor on hardness. It observed that when -106 microns sand
increases hardness decreases. So the quantity of -106 micron sand is minimum as possible.
Fig. 3.2 shows interaction plot for hardness between sand qualities (1mm, +106 micron, -106 micron) and
hardness.

+106 and hardness
-106 and hardness
1 and hardness
-106 and hardness
1 and hardness
+106 and hardness

Residual plot of hardness is shown in fig. 3.3. This layout is useful to determine whether the model meets the
assumptions of the analysis.

Fig 3.3 Residual plots for hardness
The residual plots in the graph and the interpretation of each residual plot indicate below:

Normal probability plot indicates the data are normally distributed and the variables are
influencing the response. Outliers don’t exist in the data.

Residuals versus fitted values indicate the variance is constant and a nonlinear relationship exists
as well as no outliners exist in the data.

Histogram proves the data are not skewed and no outliners exist.

Residuals versus order of the data indicate that there are systematic effects in the data due to time
or data collection order.
Table 3.3 is response table for s/n ratio, for calculation Larger is better methodology is adapted to calculating
these value. Values inside the table are the average s/n ratio for parameter in that particular level. According to
that values factor delta is calculated and higher to lower values of delta represent the rank of the parameter.
Higher rank shows that parameter is having more effect on the hardness. In this case -106 micron sand is having
rank 1st.
Table 3.3 Response table for signal to noise ratio larger is better (hardness)
Level
1 mm sand
+106 micron sand
-106 micron sand
1
29.02
29.02
29.90
2
28.76
28.80
29.05
3
29.29
29.26
28.12
Delta
0.53
0.45
1.78
Rank
2
3
1
5.1
ANOVA analysis
Table 3.4 is ANOVA table for hardness, which showing the factor degree of freedom, sum of square, mean
square, f- value and percentage contribution.
For calculating degree of freedom;
Degree of freedom = level- 1

In this study, DMAIC based Six Sigma approach implemented to optimize the processes parameters and
performance level of the casting process can be improved by using ANOVA and Taguchi method of
experimental design is used to analyze the optimum levels of individual process parameters which is affecting
the casting process. Apart from other administrative reasons, it can be concluded that the sand composition
plays a major role in productivity of the system. The following observations are made:
1.
Dust content i.e. particles below 106 micron affect the sand flow in pneumatic conveying
system; higher dust content will reduce the volume of sand conveying.
2.
High dust content also increases the volume of resin and catalyst.
3.
High dust content of the moulding sand affects the permeability of the mould thus the
quality of the casting.
4.
Dust in the system tends to clog the filters more frequently.
Dust seats on the fins of the cooling coil, hence reducing the efficiency of the cooling system, this eventually
results in to mould collapse due to hot sand. The binders loose effectiveness in hot environment.